Solutions / Use Cases
Your AppSec pentest doesn't test whether your model can be talked into misbehaving
An LLM-powered product introduces failure modes that don't exist in traditional software: prompt injection, jailbreaks that survive a system prompt, an agent that calls a tool it shouldn't, or a retrieval pipeline that leaks one customer's context into another customer's session. A standard web or API pentest tests for injection flaws and access-control bugs — it was never built to ask whether a model can be reasoned into ignoring its own safety instructions across a multi-turn conversation.
Where this actually breaks
Single-prompt “does it say something bad” testing misses failure modes that only surface across a sequenced, multi-turn conversation.
Multi-tenant AI products (one model instance serving many customers) face a uniquely dangerous failure mode: cross-tenant data leakage, where careful prompt sequencing pulls context from a different customer's session.
Agent workflows with tool-calling access (checking a balance, freezing an account, escalating to a human) can be manipulated into calling the wrong tool, or the right tool with wrong parameters.
AI governance teams need findings mapped to recognized frameworks — OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF — not a generic report translated after the fact.
How TriNetra covers it
AI Red Teaming
AI Red Teaming runs human-led, agent-assisted adversarial testing directly against the LLM endpoint, its retrieval/RAG pipeline, and any agent workflow with tool-calling access — scoping the model, the vector store, and the specific tools an agent can invoke, then executing realistic multi-turn attack scenarios rather than one-off prompts. Cross-tenant isolation gets tested explicitly, not assumed: real engagements report results as a pass/fail count rather than one “secure/not secure” verdict. Coverage tracks as a live percentage against each named framework per engagement, and multi-turn session volume is its own KPI.
AECM Assist — Pre-Launch Red Team
● COMPLETED0
Multi-turn sessions run
8 pass / 1 fail
Cross-tenant isolation checks
LLM Endpoint
Azure OpenAI GPT-4o-class — direct injection, jailbreak, instruction-override
RAG Pipeline
Customer KB + transaction-history vector store, tenant-scoped — poisoned-doc retrieval
Agent Workflow
check_balance · get_recent_transactions · freeze_card · initiate_dispute · escalate_to_human
Ish
Ish is worth naming for a specific reason: it's an AI system itself, reading across every module including AI Red Teaming, and it's held to the standard it helps you enforce elsewhere — plain-language answers from your real data, no actions taken, no tickets created, no changes executed on your behalf.
The workflow
- 1
Scope the engagement — which model endpoint, which RAG/retrieval components, which agent tools are in play.
- 2
Threat-model a plan covering injection chaining, boundary failures, and cross-component abuse.
- 3
Run multi-turn adversarial sessions, including dedicated cross-tenant isolation checks.
- 4
Findings map to OWASP LLM Top 10 / MITRE ATLAS / NIST AI RMF automatically and track to retest.
Ready when you are